Enriching captivity conditions with natural elements does not prevent the loss of wild‐like gut microbiota but shapes its compositional variation in two small mammals

Abstract As continued growth in gut microbiota studies in captive and model animals elucidates the importance of their role in host biology, further pursuit of how to retain a wild‐like microbial community is becoming increasingly important to obtain representative results from captive animals. In this study, we assessed how the gut microbiota of two wild‐caught small mammals, namely Crocidura russula (Eulipotyphla, insectivore) and Apodemus sylvaticus (Rodentia, omnivore), changed when bringing them into captivity. We analyzed fecal samples of 15 A. sylvaticus and 21 C. russula, immediately after bringing them into captivity and 5 weeks later, spread over two housing treatments: a “natural” setup enriched with elements freshly collected from nature and a “laboratory” setup with sterile artificial elements. Through sequencing of the V3–V4 region of the 16S recombinant RNA gene, we found that the initial microbial diversity dropped during captivity in both species, regardless of treatment. Community composition underwent a change of similar magnitude in both species and under both treatments. However, we did observe that the temporal development of the gut microbiome took different trajectories (i.e., changed in different directions) under different treatments, particularly in C. russula, suggesting that C. russula may be more susceptible to environmental change. The results of this experiment do not support the use of microbially enriched environments to retain wild‐like microbial diversities and compositions, yet show that specific housing conditions can significantly affect the drift of microbial communities under captivity.


| INTRODUCTION
The study of host-microbiota interactions has become integral to our understanding of animal health, ecology, and evolution (Nyholm et al., 2020). Due to the complexity of gut microbial communities, and their sensitivity to environmental factors, captivity experiments (both using laboratory and wild animals) have proven essential for detecting and measuring the detailed interactions between animals and microorganisms (Hird, 2017;Rosshart et al., 2019;Shinohara et al., 2019). Such set-ups enable the controlling and limiting of experimental factors that may influence the measured outcome, including host genetic variation (Bonder et al., 2016), developmental stage (Arrieta et al., 2014), and social interactions (Raulo et al., 2021), as well as environmental factors such as temperature (Sepulveda & Moeller, 2020), humidity (Rosenbaum et al., 2009) and diet (Bibbò et al., 2016;Maurice et al. 2015;Martínez-Mota et al., 2020;Morrison et al., 2020). However, simplified captive environments can also modify the microbiota in a variety of ways, by reducing its diversity in comparison to that of wild communities, or recruiting new bacteria that are not found in wild populations (Alberdi et al., 2021).
Such changes can differ significantly across species (Kohl et al., 2014), and might also decouple the optimal animal-microbiota balance dropping host fitness (Rosshart et al., 2017). Hence, adequate assessment of all experimental variables relating to gut microbiota dynamics is important, as deviations from healthy biotic states can lead to erroneous experimental outcomes (Beura et al., 2016;Kinross et al., 2011).
In light of such limitations and biases, researchers are actively seeking strategies to firstly maintain the original gut microbial communities of wild animals once they have been moved to captivity, and secondly, modify the gut microbiota of laboratory animals to resemble that of their wild counterparts (Rosshart et al., 2019). Since the diet is one of the factors that conditions gut microbial communities, attempts have been made to employ dietary interventions to achieve these goals (Martínez-Mota et al., 2020). An alternative strategy that has been explored is the introduction of microbes through non-dietary related environmental sources, with some studies demonstrating there can be a significant positive effect on the gut microbiome (Liu et al., 2021;Weinstein et al., 2021;Zhou et al., 2016). While the inclusion of environmental microbes in captivity experiments has been assessed to have positive outcomes, no studies have addressed this in the context of a management tool, thus making it important to assess the value of microbially enriching the environment used in captivity experiments.
To explore a new potential way to help captive animals retain wild-like gut microbiotas, we studied whether enriching captivity housing conditions with natural elements (while maintaining diet as a constant) contributes to the retention of the original (precaptivity) gut microbial community, as proxied by fecal samples, of animals captured in the wild. We carried out our experiment on two widespread non-model small mammals with differing evolutionary history and ecology: the European wood mouse (Apodemus sylvaticus -AS, order Rodentia, omnivorous diet) and the greater white-toothed shrew (Crocidura russula-CR, order Eulipotyphla, insectivorous diet). The animals were kept in captivity for 5 weeks under two different treatments: a "Natural" setup containing enrichment elements freshly collected from nature, and a "Laboratory" setup containing artificial enrichment elements. We analyzed variations in the gut microbiota from various perspectives: (i) the change in alpha diversity to assess if nature-enriched conditions contributed to maintaining wild-like gut microbial diversity; (ii) the change in beta diversity between time points and within individuals to explore whether nature-like conditions maintained a composition more similar to the original wild-like community, and (iii) the interacting effects of treatment and time on bacterial community composition to explore if the community changed in different directions over time (i.e., if the microbial community took different temporal trajectories) under the contrasting captivity conditions. Using the Hill numbers framework, we calculated neutral and phylogenetic diversity and dissimilarity indices at multiple orders of diversity (Chao et al., 2014).
This allowed us to disentangle the contribution of closely versus distantly related bacteria and rare versus common bacteria to the variations between time points and treatments for each host species.

| Animal trapping and collection
Adult AS and CR were collected across the Northern Iberian Peninsula, Europe (43.2 N, 2.2 W), from June to August 2019 (due to trapping success) over 11 field sites. Animals were trapped using Sherman traps over 3 days at each site and checked every 12 h. Traps were cleaned between locations and the baits used were a mixture of oats and tuna and a small wedge of apple. Upon successful detection, each animal was transferred into a plastic bag for species and sex identification.
Maturity of the animal was confirmed with morphometrics (e.g., body weight and length) and pelage for both species, any individuals which did not meet adult criteria or which were pregnant/lactating were excluded and released. Individuals were then individually placed into a small, microisolator cage for transfer to the ZIBA animal experimentation facilities in Zarautz, Basque Country, Spain.

| Processing and identification of shrews and mice
Before experimental inclusion, animals were checked for any signs of serious distress or ailment, and if deemed healthy to continue, an initial fecal sample (~50 mg) was collected upon arrival at the experimentation facility. Each animal was then anesthetized over a heated mat using 2% isoflurane, to allow the subcutaneous injection of a Mini HPT10 radio frequency identification chip (Biomark) into the nape of the neck for subsequent individual identification. Each individual was monitored for 5 minutes for any adverse effects before being transported into the corresponding housing enclosure.

| Housing conditions and experimental design
Animals were cohoused with conspecifics of the same sex in groups of 4-5 individuals in 840 cm 2 polycarbonate cages (Unno Type III, 38.2 × 22.0 cm). Cages were randomly assigned to two different environmental enrichment conditions, either Natural conditions (herein NC) or Laboratory conditions (herein LC). Although the cages used in both conditions contained similar three-dimensional enrichment structures, the structures themselves were created using either natural or artificial elements, respectively (Appendix A: Figure A1). NC involved the use of natural elements freshly collected from the habitats in which the animals were trapped; specifically, the soil was used as bedding, moss was provided as nesting material, and sticks and stones were used as enrichment elements. All enrichment materials for NC were collected from natural areas of low human encroachment and hence are unlikely to consist of any human features. LC included paper and wood bedding, cotton as nesting material, and 3D-printed plastic sticks and stones as enrichment elements. For both treatments, cages were cleaned and materials replaced or sterilized every week. In NC soil, nesting and enrichment items were replaced with fresh materials, while in LC fresh bedding and nesting materials were added and the enrichment materials cleaned and sterilized. With regard to diet, AS were fed a standard chow diet, while CR were fed a feed containing rice and chicken. Both diets were maintained unchanged across the whole experimental period.
Animals were kept under a strict 12 h night and day cycle, and routine cage cleaning was performed each week (replacement of bedding and nesting material), access to food was ad libitum, and food was changed daily. Environmental conditions were kept constant with an average humidity of 70%, temperature of 22°C, and 60 revolutions of air per minute by keeping the animals in an HPP 750 LIFE climate controller chamber (Memmert).

| Fecal collection
We sampled from AS (n = 15) and CR (n = 21) housed in four and five cages, respectively. The experiment was sex-biased towards male individuals (n AS = 13, n CR = 13), due to uneven capture success. Animals were split into NC (n AS = 9, n CR = 12, cages AS = 2, cages CR = 3) and LC (n AS = 6, n CR = 9, cages AS = 2, cages CR = 2) housing treatments. Fresh feces were collected from each individual immediately upon arrival at Time point 0 (herein T0; approximately 30 min -1 h after arrival to the laboratory) and day 35 (herein T1). To do so, animals were isolated into a separate sterile housing container and upon defecation, the fecal pellets (~50 mg) were collected and stored in 500 μl of DNA/RNA shield (Zymo), left at room temperature for 1 hour, and then transferred to −20°C for long-term storage until DNA extraction.

| DNA extraction and metabarcoding
DNA was extracted using a Zymo QuickDNA Fecal/Soil Microbe 96 kit (Zymo) according to manufacturer's guidelines, eluted in 50 μl of elution buffer, and immediately stored at −20°C. This involved an initial quality check for DNA concentration using a Tapestation high sensitivity kit (Agilent). Immediately after, amplification of the V3-V4 region of the 16S recombinant RNA (rRNA) gene was performed using the primers 341F:ACTCCTACGGGAGGCAGCAG (Herlemann et al., 2011) and 806R:GGACTACHVGGGTWTCTAAT (Takai & Horikoshi, 2000) using fusion tags with unique indices for downstream identification. PCR was performed in a total volume of 50 μl consisting of 25 μl of NEB Phusion ® high-fidelity PCR master mix, 4 μl of reverse and forward fusion tag primers, 30 ng of DNA extract, and ddH 2 0 up to 50 μl. PCR conditions consisted of an initial denaturation step of 98°C for 3 min, 30 cycles of denaturation at 98°C for 45 s, annealing at 55°C for 45 s, elongation at 72°C for 45 s, and lastly a final hold at 72°C for 7 min. After amplification, the PCR products were purified using Ampure beads (Agencourt) to remove small fragments and impurities. Samples were then quality and concentration checked by a Tapestation on a high sensitivity chip (Agilent) and pooled equimolar before sequencing 300 PE on a HiSeq. 2500 (Illumina) using services from BGI. Negative extraction controls were included throughout all stages of the process to control for cross-contamination.

| Bioinformatics and data analysis
Paired-end reads were first demultiplexed on unique fusion tag combinations. Immediately following this we quality-filtered the demultiplexed reads (Q > 20) using AdapterRemoval 2.3.1 (Schubert et al., 2016), and primers were removed using Cutadapt 2.10 (Martin, 2011). Low-quality reads were removed or trimmed using the filterAndTrim function implemented in DADA2 (Callahan et al., 2016). Error pattern learning and denoising of the data set were also performed using the DADA2 algorithm using default parameters (Callahan et al., 2016). Chimera removal was then performed before the generation of an ASV table consisting of ASV read counts for each sample. Reads were abundance-filtered across samples by a relative abundance of 0.01% to remove singletons and other reads that may exist due to sequencing or PCR artifacts. Taxonomy assignment was then performed by the naïve Bayesian classifier implemented in DADA2 against the SILVA 16S taxonomy database (v138). Alignment of ASV sequences was performed using Clustal Omega (Madeira et al., 2019) and subsequently, a phylogenetic tree was built in Iqtree (Minh et al., 2020). ASVs were filtered using the R package decontam (Davis et al., 2018) to detect relevant contaminants based on the prevalence algorithm.

| Diversity and compositional modeling
Gut microbiota diversity and compositional analyses were based on the Hill numbers framework. Specifically, we computed both neutral and phylogenetic diversities of orders of diversity (q value) 0, 1, and 2 using the R package Hilldiv (Alberdi, 2019). Neutral metrics do not KOZIOL ET AL. | 3 of 19 account for the degree of relatedness among ASVs, while phylogenetic metrics consider the phylogenetic correlations among ASVs when computing diversity. Differences between both dimensions of diversity metrics (neutral and phylogenetic) therefore provide insights into whether diversity variation is driven by phylogenetically close or distantly related taxa. The different orders of diversity assign different weights to the ASVs when computing diversity. A q value of 0 does not consider relative abundances but only the presence or absence of ASVs. At a q value of 1, ASVs are weighted according to their relative abundances. A q value of two overweighs abundant ASVs with respect to nonabundant ones. Comparisons between orders of diversity therefore yield information on how the evenness of ASV distribution within samples affects diversity estimation. Beta diversity between the two sampling time points (i.e., before and after the captivity period) was also measured in terms of Hill numbers by computing the Sørensen-type turnover. Similarly, Sørensen-type turnover derived from all sample pairs in the data set was used to assess the directional effect of treatment and time points in gut microbial composition (Alberdi & Gilbert, 2019;Chao et al., 2014).
Linear mixed-effect models, as implemented in the R package nlme (Pinheiro et al., 2017), were employed to assess the change in alpha diversity and beta diversity in response to experimental treatments on the gut microbiota across all individuals. In total, eight linear mixed models (Table 1) were fitted for each combination of species (i.e., AS and CR), diversity metric (i.e., neutral and phylogenetic), and also by diversity scale (i.e., alpha and beta diversity metrics). For alpha diversity models we included as fixed explanatory variables the q value (categorical factor with three levels: "0," "1," and "2"), treatment (categorical factor with two levels: "natural" and "laboratory"), time (categorical factor with two levels: "T0" and "T1") and their interactions. As several individuals were kept in each cage, and several diversity metrics were calculated from each sample, a random effect of the form "~1|Cage/Individual_ID/Sample_ID" was included in the models. Beta diversity was measured as the compositional change from T0 to T1 within each individual, hence, only treatment, q value, and their interaction were used as fixed factors and, a random effect of the form "~1|Cage/Individual_ID" was included. Linear mixed models were checked for assumptions of homoscedasticity and normality of residuals and, where assumptions were violated (e.g., alpha diversity metrics), the response variables were log-transformed. Model complexity was reduced by dropping the nonsignificant interactions between the fixed effects using likelihood ratio tests between nested models. Regardless of their significance, all main effects as well as the random effects were retained in the models as structural parts of the experimental design.
The temporal change in gut microbial composition (i.e., beta diversity between time points) may happen following independent trajectories in each individual, or directionally, following a specific trajectory across all individuals. To test the null hypothesis of no directional changes in microbiome composition from the transition of wild (T0) to day 35 (T1) we used PERMANOVA (Anderson, 2017) on pairwise dissimilarity matrices based on Sørensen-type turnover (neutral and phylogenetic, and combining different q values) using the function adonis2 in the R package "vegan" . We fitted two PERMANOVA models per type of dissimilarity matrix, one for each host species with the form adonis2(microbiome~treatment × time, strata = Individual_ID). A significant treatment × time interaction would indicate that the enrichment with natural elements led to different temporal trajectories in community composition. The magnitude of effects was quantified using the adjusted R 2 and the microbiome composition visualized using NMDS (Kruskal, 1964).
To identify the bacterial genera most severely affected by captivity conditions in each housing condition, we analyzed the data through hierarchical modeling of species communities (HMSC) at the genus level (Warton et al., 2015), as implemented in the R package HMSC (Tikhonov et al., 2020). HMSC is a hierarchical model constructed in the generalized linear model framework using Bayesian inference.
Four models were fitted, separately for each of the two species and the NC and LC. As the data were zero-inflated, we applied a hurdle model (zero-altered model) (Rose et al., 2006). This type of model consists of two parts, one modeling the presence-absence of species and the other modeling abundance conditional on presence. To fit the first model, we transformed all nonzero values in the data set into one, to create a presence-absence matrix. We applied a binomial Then, the two components of the model were fitted consecutively . The analysis was restricted to the genera that were present in at least four samples within each treatment and host species, which resulted in 82 genera for AS NC models, 64 genera for AS LC models, 96 genera for CR NC models, and 63 genera for CR LC models. This stringent criterion was used as rare species lack adequate information for taxon-specific modeling.
As fixed explanatory variables in matrix X of HMSC, we included the

| RESULTS
We analyzed 72 fecal samples from 36 animals and four negative extraction controls to account for contamination. We generated 11,425,282 sequences (114,252 ± 42,356 per sample; mean and standard deviation, respectively) with a total of 6,570,726 (65,707 ± 21,936) sequences after quality filtering (for a full breakdown see Appendix A: Table A1). From these, 8176 unique amplicon sequence variants were generated (herein ASVs), which were assigned to 31 phyla, 68 classes, 142 orders, 226 families, and 427 genera ( Figure 1). The 28 ASVs that were not assigned at least a F I G U R E 1 (a) Radial tree of life of presence/absence data at the genus level across all treatments indicating community level differences between treatments for both Apodemus sylvaticus (AS) and Crocidura russula (CR). Circular rings disseminate between Phylum, T0, natural conditions, and laboratory conditions for both species. (b) Stacked bar plots of sample pairs (T0 & T1) representing relative abundance at the community composition at the phylum level for natural conditions (yellow bar) and laboratory conditions (blue bar).
bacterial Phylum annotation were removed from downstream analyses. Decontam detected 39 ASVs as contaminants, which were also removed from all parts of the analysis (Appendix A: Table A2).
The ASV accumulation curves of all samples reached the asymptote, which confirmed sufficient sequencing depth to recover the complete microbial diversity (see Appendix A: Figure A2).

| Effects of captivity and housing treatments
Hill numbers were calculated for three orders of diversity ( PERMANOVA analyses showed that while beta-diversities were changing at similar rates and alpha diversities were showing similar decays, the composition between each treatment diverged after the captivity period (Figure 3). This separation was significant in CR as indicated by a significant interaction between treatment and time (Pseudo-F 1 = 5.171, p = 0.012, R 2 = 0.07), however, the same interaction was not significant in AS (Pseudo-F 1 = 1.297, p = 0.162, R 2 = 0.039), although some separation between treatments was visible after day 35 (Figure 3, Appendix A: Table A4).
We assessed the differential response of the most common genera to captivity in terms of probability of presence (binomial submodel of the Hurdle model) and log-abundance conditional on the presence (lognormal submodel of the Hurdle model) ( Table 2). We observed that 23% and 15% of the genera detected in AS showed a negative association with time in the binomial models in laboratory and natural conditions, respectively, whereas 8% and 9% of the genera showed positive associations (Figure 4). In the case of CR, 46% and 20% of common genera showed negative associations with time in the laboratory and natural conditions, respectively, while 25% and 30% showed positive associations. In AS abundance models, 8% and 24% of genera decreased and 6% and 1% increased in time, respectively under laboratory and natural conditions. In contrast, a F I G U R E 3 NMDS of community composition with 95% confidence intervals shaded in ellipses at Time point 1 (a, c): for A. sylvaticus and C. russula respectively representing the community composition of each individual immediately after being captured in the field (T0, day 1) and (b, d): 35 days later (T1) representing the community composition diverge between treatments of Natural (yellow) and Laboratory (blue). Additionally, we found some evidence that the NC housing conditions mitigated the loss of genus level diversity found at T0 which was not found in the T1 sample from the LC treatment T A B L E 2 HMSC results indicating the specific model used as well as the phylogenetic signal (ρ value) in species responses to time in captivity; higher ρ values indicate a higher phylogenetic signal and the 90% credible intervals not overlapping zero indicate strong evidence for its significance

F I G U R E 4 HMSC analysis showing the response of the most common genera to time in captivity, for (a) Apodemus sylvaticus and (b)
Crocidura russula. Statistical support is provided in columns for both the presence-absence/occurrence (binomial model) and abundance conditional on presence (lognormal model) models for natural conditions and laboratory conditions. Statistical support of >0.9 (red bars) was considered a significant increase of an ASV after 35 days; statistical support of <0.1 (blue bars) represents a significant decrease of an ASV; statistical support between 0.1 and 0.9 (white bars) was considered as not significantly affected by time in captivity (grey bars) represent depleted ASVs which were lost after 35 days. See the methods section for more details on how statistical support is interpreted in the Bayesian context. (Figures 1a and 4), especially with CR. Loss of genera was not observed in the NC treatment which maintained all the detected genera from their initial day 1 sample (Figure 4), albeit with reduced alpha diversities and loss of ASVs. Moreover, these associations showed phylogenetic structure in both species as determined by the ρ values calculated by the HMSC model (see Table 2).

| DISCUSSION
Continued work in assessing the role of the gut microbiota on host fitness has demonstrated that the maintenance of a biologically optimal microbiota offers many benefits, not only to the host but also to the representativeness and translatability of research (Hauffe & Barelli, 2019;Hird, 2017). In the search for useful management practices to retain wild-like gut microbial communities in animals taken into captivity, we measured the impact of two housing treatments in two small mammals and quantified several features of their associated microbial communities. We observed that increased exposure to environmental microorganisms did not significantly prevent gut microbiota diversity loss when compared to conventional experimental housing setups. Instead, our results showed that both treatments resulted in a reduction and restructuring of the gut microbiota community. However, the microbial composition changed in different directions between treatments in CR, indicating that microbial trajectories may be influenced by environmental factors in a species-specific manner.
However, in our experiment, housing animals in a (semi) sterile environment or an environment enriched with natural elements had a similar effect on microbial alpha diversity patterns. This observation is in contrast with previous studies, which demonstrated that higher diversity in the environment does yield higher complexity in the host subsystem (Sbihi et al., 2019;Zhao et al., 2020;Zhou et al., 2016).
Contrasting our studies, the main difference is likely due to the developmental stage of the analyzed animals, and the associated maturity of their gut microbial communities (Beura et al., 2016;Liu et al., 2021;Sbihi et al., 2019;Zhou et al., 2016). Our study included adults, which were shown to host a diverse microbial community before being introduced into captivity. In doing so, the starting gut composition was significantly more diverse compared to early-life conspecifics (Nemergut et al., 2013). As such, the ecological niches within the adult gut-microbiomes were likely already occupied (Langille et al., 2014;Turnbaugh et al., 2009) leading to competitive exclusion from the existing bacterial community (Baumgartner et al., 2021;Zmora et al., 2018). Thus, the lability of juvenile gut microbiomes may further promote the uptake of passively acquired environmental bacteria (Liu et al., 2021), while such a mode of acquisition seems to be negligible in adulthood, as suggested by the overall diversity loss in our experiment.
Despite the limited effect of the tested treatments at mitigating diversity loss, we did observe significant gut microbiota variation between time points in both species. Regarding alpha diversity, the observed patterns were somehow alike in the two species, even though the microbial communities associated with wild animals were radically different between AS and CR. Previous studies have demonstrated that captivity itself can significantly alter the gut microbiome of many species and may be due to a myriad of reasons, including access to nutrients, and changes in ambient temperatures/ humidity (Sepulveda & Moeller 2020;Nicholls et al., 2016;Rosenbaum et al., 2009), diet (Bibbò et al., 2016;Martínez-Mota et al., 2020;Morrison et al., 2020), which are all readily manipulated when entering captivity conditions and may also be confounded by evolutionary histories as species' responses to captivity can be hostspecific (Alberdi et al., 2021;Weinstein et al., 2021). Moreover, environmental complexity found in wild environments is not only a constituent of changes to the soil and physical surroundings but additionally to the many available bacteria in changing diets and water sources that were not tested in this experiment (Nyholm et al., 2022). As such, changing these conditions can lead to a wide range of responses, with studies reporting both significant microbial diversity increases and decreases in host responses to captivity (see [Alberdi et al., 2021] for further discussion).
Unlike overall diversity, the compositional response of the gut microbiotas to captivity and experimental treatments differed between species, which suggests that environmental access to microbes may have some effect in influencing the trajectory of the gut microbiome across different species. On the one hand, regardless of the treatment, we observed a significantly higher microbiota turnover in CR than in AS. By layering the diversity metrics at different orders of diversity with the phylogenetic information of each ASV, we were able to detect that in AS the main phylogenetic groups remained stable (Figure 2b). This indicates that when only using neutral diversity metrics, studies may omit valuable information as to whether the community changes are biologically meaningful signals or not. On the other hand, the CR gut microbiome demonstrated a clear interaction between time and treatment and separation of microbial composition based on housing conditions, which was not detected in AS. The lack of information on the microbial communities present in the natural elements prevented us to ascertain whether the observed variation was directly produced by the acquisition of environmental bacteria. However, the differences observed between AS and CR indicate that the response to the environment is likely to be highly specific-specific and that many host-specific factors may impact microbial sensitivity to environmental changes. We explored each host's response to the environment using HMSC, finding significant changes in the gut microbiota of specific genera. In most cases, genus-level associations with time were either neutral or negative suggesting that the genera which responded to time in captivity were more likely to respond KOZIOL ET AL.
negatively, that is, decreasing abundances. Despite this, we did observe that some taxa proliferated under captive conditions. In AS, while only speculative, the proliferation of Odoribacter (Hiippala et al., 2020) has been known to increase propionate production, while additionally Rikinella has been known to increase lipid metabolism and energy regulation (Gálvez-Ontiveros et al., 2020), potentially leading to positive effects on the gut health of AS. The severe drop in compositional turnover when increasing the order of diversity in phylogenetic metrics indicated that the replacement of ASVs mostly stemmed from closely related ASVs, rather than distant taxonomic groups. In contrast, we detected a highly significant, strong correlation with phylogeny in the CR data suggesting phylogenetically related genera were responding in the same way. Principally, both the presence and abundance of Proteobacteria genera significantly reduced, while bacteria within Firmicutes significantly